Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs

arXiv:2609.09270v1 Announce Type: new Abstract: Moving from quantum research and development to production-grade, fault-tolerant quantum workload execution remains one of the most significant challenges facing quantum platform builders. While Python frameworks have enabled an easy entry point for quantum algorithm design, the low-latency requirements for real-time quantum error correction (QEC) demand performance that traditional interpreted environments cannot provide. FPGAs and ASICs play a central role at these layers, but their specialized programming models make development rigid and time-consuming. CPUs, GPUs, and other accelerators introduce a different challenge: as infrastructure becomes increasingly heterogeneous, programming across different devices and their associated abstractions becomes more complex. Allowing researchers to write workloads in high-level languages that map to low-latency execution across diverse distributed target platforms will enable the development of key infrastructure for utility-scale quantum systems. For this, we introduce $\textit{Backline}$, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst. Backline allows us to design and build quantum-classical workloads for high-performance and low-latency devices, with compilation directly from a Python interface through MLIR. We demonstrate the compilation and execution of several quantum workloads with low-latency data movement across a mix of CPUs, GPUs, and FPGAs, for both local and distributed remote hardware targets, all from a vendor-agnostic Python frontend. With an AMD VPK120 FPGA board as the controller, issuing each round from its hardware-handshake engine, we measured median steady-state round-trip latencies over RoCE v2 of $2.305~\mu$s to an AMD Ryzen Threadripper PRO CPU and $4.5~\mu$s to an AMD Instinct MI210 GPU across $10^6-1$ rounds per path, demonstrating microsecond-scale synchronous co-processing.

Authors:Joseph K. L. Lee, Mehrdad Malekmohammadi, Hong-Sheng Zheng, Shuli Shu, Cheick Doumbia, Kalman Szenes, Mehran Zamani Abnili, Thomas Ainsworth, Matthew Seymour, Thomas Germain, Leonhard Neuhaus, Josh Izaac, Lee J. O'Riordan

Abstract:Moving from quantum research and development to production-grade, fault-tolerant quantum workload execution remains one of the most significant challenges facing quantum platform builders. While Python frameworks have enabled an easy entry point for quantum algorithm design, the low-latency requirements for real-time quantum error correction (QEC) demand performance that traditional interpreted environments cannot provide. FPGAs and ASICs play a central role at these layers, but their specialized programming models make development rigid and time-consuming. CPUs, GPUs, and other accelerators introduce a different challenge: as infrastructure becomes increasingly heterogeneous, programming across different devices and their associated abstractions becomes more complex. Allowing researchers to write workloads in high-level languages that map to low-latency execution across diverse distributed target platforms will enable the development of key infrastructure for utility-scale quantum systems. For this, we introduce $\textit{Backline}$, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst. Backline allows us to design and build quantum-classical workloads for high-performance and low-latency devices, with compilation directly from a Python interface through MLIR. We demonstrate the compilation and execution of several quantum workloads with low-latency data movement across a mix of CPUs, GPUs, and FPGAs, for both local and distributed remote hardware targets, all from a vendor-agnostic Python frontend. With an AMD VPK120 FPGA board as the controller, issuing each round from its hardware-handshake engine, we measured median steady-state round-trip latencies over RoCE v2 of $2.305~\mu$s to an AMD Ryzen Threadripper PRO CPU and $4.5~\mu$s to an AMD Instinct MI210 GPU across $10^6-1$ rounds per path, demonstrating microsecond-scale synchronous co-processing.

Quantum Physics (quant-ph); Distributed, Parallel, and Cluster Computing (cs.DC); Programming Languages (cs.PL)

arXiv:2609.09270 [quant-ph]

(or arXiv:2609.09270v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.09270 arXiv-issued DOI via DataCite (pending registration)

analiza aiqbench

Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs

Subiecte conexe